def main():
run_many.build()
command = './main {} {}'
pool = multiprocessing.Pool(12)
def make_candidate(*args):
def gen():
while True:
tasks = [
(command.format(*args), random.randrange(1, 1000000000))
for _ in range(N)]
results = list(pool.imap(run_many.worker, tasks))
for result in results:
yield result['adjusted_score'] * 100
return args, gen()
#candidates = dict(map(make_candidate, [(0.85, 0.65), (2, 1)]))
candidates = []
for _ in range(50):
candidates.append(make_candidate(
random.uniform(0.5, 1),
random.uniform(0.4, 0.9),
))
candidates.append(make_candidate(
random.uniform(1.5, 2.5),
random.uniform(0.7, 1.8),
))
thompson_tournament(dict(candidates))
def main():
run_many.build()
with open('data/seeds_by_bucket.json') as fin:
seeds_by_bucket = json.load(fin)
seeds_by_bucket = {tuple(k): v for k, v in seeds_by_bucket}
pool = multiprocessing.Pool(12)
candidate_labels = [(2, 1, 1), (0.643, 0.689, 1)]
for _ in range(50):
candidate_labels.append((
round(0.001 * random.randrange(600, 2000), 3),
round(0.001 * random.randrange(600, 2000), 3),
round(0.001 * random.randrange(500, 2000), 3),
))
opt = Optimizer(seeds_by_bucket, candidate_labels)
while True:
print('*' * 50)
opt.show()
opt.advance(pool.imap)
